Is the implementation of dry stacking for tailings storage increasing? A Southern African perspective
Bibliographic record
Abstract
It is good practice in the early phases of a new mine design, or when a new tailings storage facility (TSF) is required at an existing mine, to consider alternatives and carry out trade-off studies for tailings storage. These studies should include multiple sites and at least two disposal methods or technologies with the aim of identifying the best tailings management system for the project, generally the most cost-effective, socially and environmentally acceptable system. Dry stacking is gaining credibility and is seen as a preferred technology to manage project specific risks for various reasons: lower risk of failure, increased water conservation and water cost saving, project stakeholders and environmental considerations, better geochemical mitigation, and possible improvement in metal recovery during filtration through additional mineral dissolution. In some cases, the drivers for considering dry stacking are obvious, such as a mine located in a dry climate or new regulations, but in other places this is less obvious. This paper evaluates the outcomes of a number of such trade-off studies mostly in Southern Africa or arid regions of Africa, which include: The paper also looks at two mines where filtered tailings has been implemented, their overall TSF operating and stability performance, as well as opportunities and challenges of the technology. No names of the mines are included, as the focus is on whether there is an increased move towards dry stacking, and what obstacles are being experienced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".